航空学报2025，Vol.46Issue(14)：46-60,15.DOI:10.7527/S1000-6893.2024.30910
基于NSGA-Ⅲ-SAM算法的冲压发动机喷管性能预测
Scramjet nozzle performance prediction based on NSGA-Ⅲ-SAM algorithm
摘要
Abstract
The exhaust characteristics of a nozzle directly affect the overall performance of the scramjet engine.It is crucial to effectively predict the nozzle performance to prevent drastic changes for the stable operation of the engine.Numerical simulations of three-dimensional asymmetric nozzles under different flight conditions were conducted to build a dataset for predicting nozzle performance at various Mach numbers and nozzle pressure ratios.Considering the limi-tations of traditional multi-objective optimization algorithms,a Non-dominated Sorting Genetic Algorithm-Ⅲ-Simulated Annealing Mutation(NSGA-Ⅲ-SAM)was proposed to extract the optimal wall pressure measurement points for the nozzle.By using the optimal pressure characteristic data as input and the axial thrust coefficient,pitching moment co-efficient,and lift coefficient as outputs,a nozzle performance parameter prediction model based on the One Dimension-Convolutional Neural Network(1D-CNN)was established and validated by the data of over-expanded states at the Mach numbers from 4.5 to 6.0.The results show that the optimal pressure positions extracted by the NSGA-Ⅲ-SAM algorithm enable the model to have high-precision and rapid prediction performance,with the overall average absolute error of all performance parameters being within 0.5%,the maximum absolute error not exceeding 0.8%,and the average prediction time being only about 0.6 ms.The proposed prediction model and method provide a reliable technical foundation for monitoring nozzle performance and adjusting exhaust conditions.关键词
性能预测/三维非对称喷管/NSGA-Ⅲ-SAM算法/壁面最优压力特征/1D-CNN模型Key words
performance prediction/three-dimensional asymmetric nozzle/NSGA-Ⅲ-SAM algorithm/wall-optimized pressure measurement point/1D-CNN model分类
航空航天引用本文复制引用
闵科,蔡泽君,张加乐,朱呈祥..基于NSGA-Ⅲ-SAM算法的冲压发动机喷管性能预测[J].航空学报,2025,46(14):46-60,15.基金项目
国家自然科学基金(U21B6003,12202372) （U21B6003,12202372）
1912项目 National Natural Science Foundation of China(U21B6003,12202372) （U21B6003,12202372）
1912 Project （）
